title: Extracting between-pathway models from E-MAP interactions using expected graph compression.
abstract: Abstract Genetic interactions (such as synthetic lethal interactions) have become quantifiable on a large-scale using the epistatic miniarray profile (E-MAP) method. An E-MAP allows the construction of a large, weighted network of both aggravating and alleviating genetic interactions between genes. By clustering genes into modules and establishing relationships between those modules, we can discover compensatory pathways. We introduce a general framework for applying greedy clustering heuristics to probabilistic graphs. We use this framework to apply a graph clustering method called graph summarization to an E-MAP that targets yeast chromosome biology. This results in a new method for clustering E-MAP data that we call Expected Graph Compression (EGC). We validate modules and compensatory pathways using enriched Gene Ontology annotations and a novel method based on correlated gene expression. EGC finds a number of modules that are not found by any previous methods to cluster E-MAP data. EGC also uncovers core submodules contained within several previously found modules, suggesting that EGC can reveal the finer structure of E-MAP networks.
authors.affiliation: Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
authors.forename: David R
authors.initials: DR
authors.lastname: Kelley
authors.forename: Carl
authors.initials: C
authors.lastname: Kingsford
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D044962:Data Compression / Q000379:methods; D004843:Epistasis, Genetic; D015966:Gene Expression Regulation, Fungal; D005800:Genes, Fungal; D008957:Models, Genetic; D058977:Molecular Sequence Annotation; D012441:Saccharomyces cerevisiae / Q000235:genetics